Abstract

The motivation of this paper is 3-fold. Firstly, we apply a Multi-Layer Perceptron (MLP), a Recurrent Neural Network (RNN) and aPsi-Sigma Network (PSN) architecture in a forecasting and trad-ing exercise on the EUR/USD, EUR/GBP and EUR/CHF exchangerates and explore the utility of Kalman Filter, Genetic Programming(GP) and Support Vector Regression (SVR) algorithms as forecastingcombination techniques. Secondly, we introduce a hybrid leveragefactor based on volatility forecasts and market shocks and studyif its application improves the trading performance of our mod-els. Thirdly, we introduce a specialized loss function for NeuralNetworks (NNs) in financial applications. In terms of our results,the PSN from the individual forecasts and the SVR from our forecastcombination techniques outperform their benchmarks in statisti-cal accuracy and trading efficiency. We also note that our tradingstrategy is successful, as it increased the trading performance ofmost of our models, while our NNs loss function seems promising.